Intrinsic relationship of outdoor activities between climate change and coastal management: Fresh evidence from the spatial analysis of China.

By utilizing the mediation effect model and the spatial Durbin model, this research investigates the influence that environmental restrictions have had on marine pollution in 38 coastal prefecture-level cities from the years 2000-2018. In order to gain a comprehensive understanding of the effect that environmental legislation has on contamination in offshore regions as well as its primary goal, the research takes a variety of different approaches into consideration. Following are the findings from the study; Firstly, pollution levels in coastal marine areas tend to rise at first and then fall when environmental laws are enacted, illustrating a non-linear pattern known as an inverted "U" shape. In order to improve the maritime environment through environmental legislation, it is crucial to support new green technologies. There is a "U" shaped linkage amongst environmental legislation and development of environmentally friendly technologies. Spatial spillover effects may allow for the regulation of coastal city environments to affect marine pollution in neighboring areas. Secondly, there is also an inverted "U" pattern visible in the impact trajectory of this effect. According to the results of this research, it is crucial to set up a strict and factually sound regulatory framework in the field of marine environmental governance. It is also suggested that local context be taken into account while crafting environmental regulating regulations. Also, it's crucial to promote development, dissemination, and use of green technology by completely capitalizing on the innovation's conduction effect. Thirdly, promoting cooperation efforts among areas to avoid and control such pollution is essential, and the transfer and management of offshore pollution between regions must be a top priority.

Assessment of spatial variation in lung cancer incidence and air pollutants: spatial regression modeling approach.

A notable finding is that Kerala's capital Thiruvananthapuram has shown an increasing trend in lung cancer (LC) incidence. Long-term exposure to air pollution is a significant environmental risk factor for LC. This study investigated the spatial association between LC and exposure to air pollutants in Thiruvananthapuram, using Spatial Lag Model (SLM), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR). The results showed that overall LC incidence rate was 111 per 105 males (age >60 years), whereas spatial distribution map revealed that 48% of the area had an incidence rate greater than 150. The results revealed a significant association between PM2.5 and LC. SLM was identified as the best model that predicted 62% variation in LC. GWR model improved model performance and made better local predictions in the southeastern parts of the study area. This study explores the effectiveness of spatial regression techniques for dealing spatial effects and pinpointing high-risk areas.

Explaining the role of land use changes on land surface temperature in an arid and semi-arid metropolitan area with multi-scale spatial regression analysis.

Urban Heat Islands (UHIs), Land Surface Temperature (LST), and Land Use Land Cover (LULC) changes are critical environmental concerns that require continuous monitoring and assessment, especially in cities within arid and semi-arid (ASA) climates. Despite the abundance of research in tropical, Mediterranean, and cold climates, there is a significant knowledge gap for cities in the Middle East with ASA climates. This study aimed to examine the effects of LULC change, population, and wind speed on LST in the Mashhad Metropolis, a city with an ASA climate, over a 30-year period. The research underscores the importance of environmental monitoring and assessment in understanding and mitigating the impacts of urbanization and climate change. Our research combines spatial regression models, multi-scale and fine-scale analyses, seasonal and city outskirts considerations, and long-term change assessments. We used Landsat satellite imagery, a crucial tool for environmental monitoring, to identify LULC changes and their impact on LST at three scales. The relationships were analyzed using Ordinary Least Squares (OLS) and Spatial Error Model (SEM) regressions, demonstrating the value of these techniques in environmental assessment. Our findings highlight the role of environmental factors in shaping LST. A decrease in vegetation and instability of water bodies significantly increased LST over the study period. Bare lands and rocky terrains had the most substantial effect on LST. At the same time, built-up areas resulted in Urban Cooling Islands (UCIs) due to their lower temperatures compared to surrounding bare lands. The Normalized Difference Vegetation Index (NDVI) and Dry Bare-Soil Index (DBSI) were the most effective indices impacting LST in ASA regions, and the 30×30 m2 micro-scale provides more precise results in regression models, underscoring their importance in environmental monitoring. Our study provided a comprehensive understanding of the relationship between LULC changes and LST in an ASA environment, contributing significantly to the literature on environmental change in arid regions and the methodologies for monitoring such changes. Future research should aim to validate and expand additional LST-affecting factors and test our approach and findings in other ASA regions, considering the unique characteristics of these areas and the importance of tailored environmental monitoring and assessment approaches.

Differential equity in access to public and private coastal infrastructure in the Southeastern United States.

Despite the ubiquity of coastal infrastructure, it is unclear what factors drive its placement, particularly for water access infrastructure (WAI) that facilitates entry to coastal ecosystems such as docks, piers, and boat landings. The placement of WAI has both ecological and social dimensions, and certain segments of coastal populations may have differential access to water. In this study, we used an environmental justice framework to assess how public and private WAI in South Carolina, USA are distributed with respect to race and income. Using publicly available data from State agencies and the US Census Bureau, we mapped the distribution of these structures across the 301 km of the South Carolina coast. Using spatially explicit analyses with high resolution, we found that census block groups (CBGs) with lower income are more likely to contain public WAI, but racial composition has no effect. Private docks showed the opposite trends, as the abundance of docks is significantly, positively correlated with CBGs that have greater percentages of White residents, while income has no effect. We contend that the racially unequal distribution of docks is likely a consequence of the legacy of Black land loss, especially of waterfront property, throughout the coastal southeast during the past half-century. Knowledge of racially uneven distribution of WAI can guide public policy to rectify this imbalance and support advocacy organizations working to promote public water access. Our work also points to the importance of considering race in ecological research, as the spatial distribution of coastal infrastructure directly affects ecosystems through the structures themselves and regulates which groups access water and what activities they can engage in at those sites.

The impact of built and socio-economic environment factors on Covid-19 transmission at the ZIP-code level in Florida.

Most studies have explored the Covid-19 outbreak by mainly focusing on restrictive public policies, human health, and behaviors at the macro level. However, the impacts of built and socio-economic environments, accounting for spatial effects on the spread at the local levels, have not been thoroughly studied. In this study, the relationships between the spatial spread of the virus and various indicators of the built and socio-economic environments are investigated, using Florida ZIP-code data on accumulated cases before large-scale vaccination campaigns began in 2021. Spatial regression models are used to account for the spatial dependencies and interactions that are core factors in Covid-19 spread. This study reveals both the spillover dynamics of the coronavirus spread at the ZIP code level and the existence of spatial dependencies among the unobserved variables represented by the error term. In addition, the findings show a positive association between the expected number of Covid-19 cases and specific land uses, such as education facilities and retail densities. Finally, the study highlights critical socio-economic characteristics causing a substantial increase in Covid-19 spread. Such results could help policymakers, public health experts, and urban planners design strategies to mitigate the spread of future Covid-19-like diseases.

Identifying the coupling coordination relationship between urbanization and forest ecological security and its impact mechanism: Case study of the Yangtze River Economic Belt, China.

Boosting the coordination and symbiosis of urbanization and forest ecological security is notably critical for promoting regional green and sustainable development and achieving emission peak and carbon neutrality goals. However, there was still a lack of in-depth analysis of the coupling coordination relationship between urbanization and forest ecological security and its impact mechanism. On the basis of the data from 844 counties in the Yangtze River Economic Belt, this paper explored the spatial differences and influencing factors of the coupling coordination degree of urbanization and forest ecological security. The results manifested that: i) There were apparent spatial disparities in the urbanization index, forest ecological security index, comprehensive index, coupling degree and coupling coordination degree of the Yangtze River Economic Belt. Among them, the spatial pattern of coupling coordination degree had a strong consistency with urbanization index, that is, areas with higher urbanization index also had higher coupling coordination degree. ii) Based on coupling feature identification, it was found that 249 'problem areas' were mainly located in Yunnan Province, southeastern Guizhou Province, central Anhui Province, and central and eastern Jiangsu Province. The main factor for the formation was due to the lag of urbanization in coordinated development. iii) Among the socioeconomic indicators, population structure (0.136), per capita year-end financial institutions loan balance (0.409) and per capita fixed asset investment (0.202) all had a positive impact on coupling coordination degree, while location conditions (-0.126) had a negative impact. Among the natural indicators, soil organic matter (-0.212) and temperature (-0.094) had a negative impact on coupling coordination degree. iv) During the process of coordinated development, it was necessary to increase financial investment and financial support, actively formulate policies to attract talents, enhance the education and publicity of ecological civilization, and develop a green circular economy. The above measures can promote the harmonious development of urbanization and forest ecological security in the Yangtze River Economic Belt.

Influence of spatial distribution pattern of buildings on the distribution of urban gaseous pollutants.

Buildings are the main component of urban, and their three-dimensional spatial patterns affect meteorological conditions and consequently, the spatial distribution of gaseous pollutants (CO, NO, NO2, and SO2). This study uses the Jinan Central District as the study area and constructs a building spatial distribution index system based on DEM, urban road network, and building big data. ANOVA and spatial regression models were used to study the effects of building spatial distribution indicators on the distribution of gaseous pollutants along with their spatial heterogeneity. The results showed that (1) the effects of most of spatial distribution indexes of building on the concentration distribution of the four gaseous pollutants were significant, with one-way ANOVA outcomes reaching a significance level of 0.01 or more. The DEM mean, building altitude, and their interaction with other building spatial distribution indicators are important factors affecting the distribution of gaseous pollutants; The interaction of other three-factor indicators did not have a significant effect on the distribution of gaseous pollutant concentrations. (2) The spatial distribution of CO and NO2 is mainly influenced by the indicators of the spatial distribution of buildings in this study unit, and the effects of CO and NO2 concentrations in adjacent study units are the result of the action of stochastic factors. The NO and SO2 concentrations are influenced by the spatial distribution index of buildings in this study unit, the neighborhood homogeneity index, and NO and SO2 concentrations. (3) Spatial heterogeneity was observed in the effects of building spatial distribution indicators on the concentrations of different pollutants. The GWR models constructed using CO and NO concentrations and building spatial distribution indicators were well fitted globally and locally. The CO and NO concentrations were negatively correlated with the mean topographic elevation and NO concentrations were correlated with building density.

Association between urban greenspace, tree canopy cover and intentional deaths: An exploratory geospatial analysis.

Greenspaces can provide restorative experiences, offer opportunities for outdoor recreation, and reduce mental fatigue; all of which may improve community health and safety. Yet few studies have examined the neighborhood-level benefits of greenspace in reducing violent deaths. This study explored the association between three distinct greenspace metrics: public greenspace quantity, public greenspace accessibility, neighborhood tree canopy cover, and intentional deaths (i.e., homicides and suicides). Generalized linear models and spatial error models investigated the association between greenspace, tree canopy and intentional deaths in three geographically distinct cities in North Carolina. Results revealed that increased neighborhood greenspace accessibility and tree canopy cover were associated with reduced intentional deaths in all three urban areas. Neighborhood greenspace accessibility was the most protective factor across all study areas. The relationship between neighborhood greenspace accessibility and intentional deaths was more significant for non-firearm deaths as compared to firearm deaths, indicating that weapon type may be an important consideration for neighborhood greenspace interventions. Compared to predominantly White neighborhoods, predominantly Black neighborhoods had higher rates of homicide in Asheville and Durham and higher rates of suicide in Charlotte. Future policy and research should focus on improving equitable access to existing and future greenspaces, especially in primarily Black neighborhoods.

Equality and social determinants of spatial accessibility, availability, and affordability to primary health care in Hong Kong, a descriptive study from the perspective of spatial analysis.

OBJECTIVE: Primary health care (PHC) is widely perceived to be the backbone of health care systems. Since the outbreak of COVID-19, PHC has not only provided primary medical services, but also served as a grassroots network for public health. Our research explored the accessibility, availability, and affordability of primary health care from a spatial perspective, to understand the social determinants affecting access to it in Hong Kong. METHOD: This constitutes a descriptive study from the perspective of spatial analysis. The nearest neighbor method was used to measure the geographic accessibility of PHC based on the road network. The 2SFCA method was used to measure spatial availability and affordability to primary health care, while the SARAR model, Spatial Error model, and Spatial Lag model were then constructed to explain potential factors influencing accessibility and availability of PHC. RESULTS: In terms of accessibility, 95% of residents in Hong Kong can reach a PHC institution within 15 minutes; in terms of availability, 83% of residents can receive PHC service within a month; while in terms of affordability, only 32% of residents can afford PHC services with the support of medical insurance and medical voucher. In Hong Kong, education status and household income show a significant impact on accessibility and availability of PHC. Regions with higher concentrations of residents with post-secondary education receive more PHC resources, while regions with higher concentrations of high-income households show poorer accessibility and poorer availability to PHC. CONCLUSION: The good accessibility and availability of primary health care reflects that the network layout of existing PHC systems in Hong Kong is reasonable and can meet the needs of most residents. No serious gap between social groups further shows equality in resource allocation of PHC in Hong Kong. However, affordability of PHC is not ideal. Indeed, narrowing the gap between availability and affordability is key to fully utilizing the capacity of the PHC system in Hong Kong. The private sector plays an important role in this, but the low coverage of medical insurance in outpatient services exacerbates the crowding of public PHC and underutilization of private PHC. We suggest diverting patients from public to private institutions through medical insurance, medical vouchers, or other ways, to relieve the pressure on the public health system and make full use of existing primary health care in Hong Kong.

Magnitude of visceral leishmaniasis and HIV coinfection and association with social determinants of health in the Northeast region of Brazil: a retrospective, spatiotemporal model (2010-2018).

The Northeast region of Brazil (NRB) includes the states with the highest prevalence of visceral leishmaniasis (VL), as well as those with significant increases in HIV cases. This study aims to analyze the spatiotemporal patterns of VL-HIV coinfection and its association with the social determinants of health (SDH) in the NRB. Time trend analysis and Bayesian spatial statistical inferences, Moran's autocorrelation, and retrospective space-time scanning were performed. Spatial regression modelling was used to build an explanatory model for the occurrence of VL-HIV coinfection within NRB. A total of 1550 cases of VL-HIV coinfection were confirmed. We observed a higher prevalence among males (1232; 83%), individuals aged from 20 to 59 years (850; 54.8%), non-white skin color (1,422; 91.7%), and with low education (550; 35.48%). NRB showed an increasing and significant trend in the detection rate of coinfection (APC, 5.3; 95% CI, 1.4 to 9.4). The states of Maranhão and Piauí comprised the high-risk cluster. The SDH that most correlated with the occurrence of coinfection were poor housing, low income, and low education. VL-HIV is dispersed in the NRB but chiefly affects states with greater social vulnerability. Taken together, these findings reinforce the necessity to implement surveillance strategies that will contribute to the reduction of cases in these populations.

Urbanisation and ecosystem health in the Middle Reaches of the Yangtze River urban agglomerations, China: A U-curve relationship.

Rapid urbanisation in global urban agglomerations has caused serious disturbances to the structure, function, and health state of ecosystems. Investigating the driving mechanisms behind the impact of urbanisation level (UL) on ecosystem health index (EHI) is important for constructing ecological civilisation and developing superior urban agglomerations in China. However, no in-depth studies exist on these mechanisms in various urban agglomerations, which makes formulation and implementation of effective ecosystem management and control policies difficult. In this study, we estimated UL and EHI based on multisource data, and a set of spatial regression models were then used to analyse the driving mechanisms at global and local scales in the Middle Reaches of the Yangtze River urban agglomeration (MRYRUA) in China between 1995 and 2015. Our results demonstrated that EHIs in the MRYRUA were 0.627, 0.613, and 0.610 in 1995, 2005, and 2015, respectively, with 2.71% decreases during the study period. The EHI in the surrounding mountainous regions was considerably higher than that in the plains. There was a significant spatial dependence between the UL and EHI. Low UL and high EHI, high UL and low EHI, and low UL and low EHI were the dominant relationship types in the MRYRUA (25.61%, 11.83%, and 11.27%, respectively). A 10% increase in UL resulted in 1.79%, 2.50%, and 2.99% decrease in EHI for each reference year in the spatial error model with lag dependence model. A U-shaped relationship was identified between UL and EHI in different urban agglomerations and cities of different administrative levels. Therefore, the results of this study can provide a scientific basis for the formulation of macro-control policies and locally specific control policies for ecosystem protection in the MRYRUA.

Exploring the socioeconomic drivers of COVID-19 mortality across various spatial regimes.

Identifying the socioeconomic drivers of COVID-19 deaths is essential for designing effective policies and health interventions. However, how the significance and impact of these factors varies across different spatial regimes has been scantly explored. In this ecological cross-sectional study, we apply the spatial lag by regimes regression model to examine how the socioeconomic and health determinants of COVID-19 death rate vary across (a) metropolitan vs. non-metropolitan, (b) shelter-in-place vs. no-shelter-in-place order, and (c) Democratic vs. Republican US counties. A total of 20 variables were studied across 3108 counties in the contiguous US for the first year of the pandemic (6 February 2020 to 5 February 2021). The results show that the COVID-19 death rate not only depends on a complex interplay of the population demographic, socioeconomic and health-related characteristics, but also on the spatial regime that the residents live, work and play. Household median income, household size, percentage of African Americans, percentage aged 40-59 and heart disease mortality are significant to metropolitan but not to non-metropolitan counties. We identified lack of insurance access as a significant driver across all regimes except for Democratic. We also showed that the political orientation of the governor might have impacted COVID-19 death rates due to the public response (i.e., shelter-in-place vs. no-shelter-in-place order). The proposed analysis allows for understanding the socioeconomic context in which public health policies can be applied, and importantly, it presents how COVID-19 death related factors vary across different spatial regimes.

Do the determinants of COVID-19 transmission differ by epidemic wave? Evidence from U.S. counties.

This paper uses data from the United States to examine determinants of the spread of COVID-19 during three different epidemic waves. We address how sociodemographic and economic attributes, industry composition, density, crowding in housing, and COVID-19-related variables are associated with the transmission of COVID-19. After controlling for spatial autocorrelation, our findings indicate that the percentage of people in poverty, number of restaurants, and percentage of workers teleworking were associated with the COVID-19 incidence rate during all three waves. Our results also show that dense areas were more vulnerable to the transmission of COVID-19 after the first epidemic wave. Regarding the density of supermarkets, our study elaborates the negative aspects of wholesale retail stores, which likely provide a vulnerable place for virus transmission. Our results suggest that sociodemographic and economic attributes were the determinants of the early phase of the pandemic, while density showed positive association with the transmission during subsequent waves. We provide implications for regions serving as gateway cities with high density and number of population. To add, we further provide evidence that non-pharmaceutical interventions in the early stage may mitigate the virus transmission.

Spatial analysis of colorectal cancer outcomes and socioeconomic factors in Virginia.

BACKGROUND: Colorectal cancer (CRC) disparities vary by country and population group, but often have spatial features. This study of the United States state of Virginia assessed CRC outcomes, and identified demographic, socioeconomic and healthcare access contributors to CRC disparities. METHODS: County- and city-level cross-sectional data for 2011-2015 CRC incidence, mortality, and mortality-incidence ratio (MIR) were analyzed for geographically determined clusters (hotspots and cold spots) and their correlates. Spatial regression examined predictors including proportion of African American (AA) residents, rural-urban status, socioeconomic (SES) index, CRC screening rate, and densities of primary care providers (PCP) and gastroenterologists. Stationarity, which assesses spatial equality, was examined with geographically weighted regression. RESULTS: For incidence, one CRC hotspot and two cold spots were identified, including one large hotspot for MIR in southwest Virginia. In the spatial distribution of mortality, no clusters were found. Rurality and AA population were most associated with incidence. SES index, rurality, and PCP density were associated with spatial distribution of mortality. SES index and rurality were associated with MIR. Local coefficients indicated stronger associations of predictor variables in the southwestern region. CONCLUSIONS: Rurality, low SES, and racial distribution were important predictors of CRC incidence, mortality, and MIR. Regions with concentrations of one or more factors of disparities face additional hurdles to improving CRC outcomes. A large cluster of high MIR in southwest Virginia region requires further investigation to improve early cancer detection and support survivorship. Spatial analysis can identify high-disparity populations and be used to inform targeted cancer control programming.

Understanding Pediatric Surgery Cancellation: Geospatial Analysis.

BACKGROUND: Day-of-surgery cancellation (DoSC) represents a substantial wastage of hospital resources and can cause significant inconvenience to patients and families. Cancellation is reported to impact between 2% and 20% of the 50 million procedures performed annually in American hospitals. Up to 85% of cancellations may be amenable to the modification of patients' and families' behaviors. However, the factors underlying DoSC and the barriers experienced by families are not well understood. OBJECTIVE: This study aims to conduct a geospatial analysis of patient-specific variables from electronic health records (EHRs) of Cincinnati Children's Hospital Medical Center (CCHMC) and of Texas Children's Hospital (TCH), as well as linked socioeconomic factors measured at the census tract level, to understand potential underlying contributors to disparities in DoSC rates across neighborhoods. METHODS: The study population included pediatric patients who underwent scheduled surgeries at CCHMC and TCH. A 5-year data set was extracted from the CCHMC EHR, and addresses were geocoded. An equivalent set of data >5.7 years was extracted from the TCH EHR. Case-based data related to patients' health care use were aggregated at the census tract level. Community-level variables were extracted from the American Community Survey as surrogates for patients' socioeconomic and minority status as well as markers of the surrounding context. Leveraging the selected variables, we built spatial models to understand the variation in DoSC rates across census tracts. The findings were compared to those of the nonspatial regression and deep learning models. Model performance was evaluated from the root mean squared error (RMSE) using nested 10-fold cross-validation. Feature importance was evaluated by computing the increment of the RMSE when a single variable was shuffled within the data set. RESULTS: Data collection yielded sets of 463 census tracts at CCHMC (DoSC rates 1.2%-12.5%) and 1024 census tracts at TCH (DoSC rates 3%-12.2%). For CCHMC, an L2-normalized generalized linear regression model achieved the best performance in predicting all-cause DoSC rate (RMSE 1.299%, 95% CI 1.21%-1.387%); however, its improvement over others was marginal. For TCH, an L2-normalized generalized linear regression model also performed best (RMSE 1.305%, 95% CI 1.257%-1.352%). All-cause DoSC rate at CCHMC was predicted most strongly by previous no show. As for community-level data, the proportion of African American inhabitants per census tract was consistently an important predictor. In the Texas area, the proportion of overcrowded households was salient to DoSC rate. CONCLUSIONS: Our findings suggest that geospatial analysis offers potential for use in targeting interventions for census tracts at a higher risk of cancellation. Our study also demonstrates the importance of home location, socioeconomic disadvantage, and racial minority status on the DoSC of children's surgery. The success of future efforts to reduce cancellation may benefit from taking social, economic, and cultural issues into account.

Big data spatial analysis of campers' landscape preferences: Examining demand for amenities.

Outdoor recreation decision-making has received significant research interest over the last fifty years. In the context of campsite choice, this previous research has almost exclusively used stated preference data and aspatial methods to understand decision-making. This present research seeks to understand how recreationists reach decisions on the selection of campsites and what aspects of the recreational setting drive demand through an examination of a big dataset of revealed preference data using a spatial regression. Specifically, we examine which managerial, social, and ecological aspects of the setting influence demand for campsites in Zion National Park's (USA) Watchman Campground using reservation data from the Recreation Information Database (RIDB). Results indicate that price, access to electricity, ease of access, and proximity to the Virgin River are significantly predictive of demand. Study implications for park management, including campsite allocation and distributive justice, are provided. Additionally, implications for future research methodology, including the use of transaction-style big data in protected area management research, are discussed.

A spatial epidemiology case study of mentally unhealthy days (MUDs): air pollution, community resilience, and sunlight perspectives.

The main objective of this spatial epidemiologic research is to gain greater insights into the geographic dimension displayed by the different duration of mentally unhealthy days (MUDs) across U.S. counties. Mentally unhealthy days (MUDs) are studied in entire cross counties for year of 2014. Using Behavioural Risk Factor Surveillance System (BRFSS) data in 2014, we examine main factors of mental health hazard including health behaviour, clinical care, socioeconomic and physical environment, demographic, community resilience, and extreme climatic conditions. In this study, we take complex design factors such as clustering, stratification and sample weight in the BRFSS data into account by using Complex Samples General Linear Model (CSGLM). Then, spatial regression models, spatial lag and error models, are applied to examine spatial dependencies and heteroscedasticity. Results of the geographic analyses indicate that counties with lower air pollution (PM2.5), higher community resilience (social, economic, infrastructure, and institutional resilience), and higher sunlight exposure had significantly lower average number of MUDs reported in the past 30 days. These findings suggest that policy makers should take air pollution, community resilience, and sunlight exposure into account when designing environmental and health policies and allocating resources to more effectively manage mental health problems.

Hip fractures and characteristics of living area: a fine-scale spatial analysis in France.

We investigated the association between hip fracture incidence and living area characteristics in France. The spatial distribution of hip fracture incidence was heterogeneous and there was a significant relationship between social deprivation, urbanization, health access, and hip fracture risk. INTRODUCTION: Several studies have shown great disparities in spatial repartition of hip fractures (HF). The aim of the study was to analyze the association between HF incidence and characteristics of the living area. METHODS: All patients aged 50 or older, living in France, who were hospitalized for HF between 2012 and 2014 were included, using the French national hospital discharge database. Standardized incidence ratio (SIR) was calculated for each spatial unit and adjusted on age and sex. An ecological regression was performed to analyze the association between HF standardized incidence and ecological variables. We adjusted the model for neighborhood spatial structure. We used three variables to characterize the living areas: a deprivation index (French-EDI); healthcare access (French standardized index); land use (percentage of artificialized surfaces). RESULTS: A total of 236,328 HF were recorded in the French hospital national database, leading to an annual HF incidence of 333/100,000. The spatial analysis revealed geographical variations of HF incidence with SIR varying from 0.67 (0.52; 0.85) to 1.45 (1.23; 1.70). There was a significant association between HF incidence rates and (1) French-EDI (trend p = 0.0023); (2) general practitioner and nurse accessibility (trend p = 0.0232 and p = 0.0129, respectively); (3) percentage of artificialized surfaces (p < 0.0001). CONCLUSION: The characteristics of the living area are associated with significant differences in the risk of hip fracture of older people.

Exploration of population ecological factors related to the spatial heterogeneity of dengue fever cases diagnosed through a national network of laboratories in India, 2017.

Background & objectives: Dengue virus (DENV) transmission is known to be influenced by the environmental conditions. During 2017, the Viral Research and Diagnostic Laboratories (VRDLs) tested 78,744 suspected dengue fever (DF) patients, of whom, 21,260 were laboratory confirmed. The objectives of the study were to evaluate the hypothesis that spatial heterogeneity existed for DF patients and to identify significant determinants of DENV transmission in various districts across the Indian States during 2017. Methods: Laboratory confirmed DF cases were analysed from 402 districts spread across the Indian States. The determinants for DF transmission included in the model were population density, proportion of population living in rural areas, proportion o f forest cover area to the total geographical area, proportion of persons not able to read and write and who were aged greater than seven years; the climatic variables considered were minimum, maximum and average temperature, precipitation and cumulative rainfall. The spatial heterogeneity was assessed using spatial regression analysis. Results: DF cases showed strong spatial dependency, with Moran's I=4.44 (P <0.001). The robust measure for spatial lag (6.55; P=0.01) was found to be the best model fit for the data set. Minimum temperature and cumulative rainfall were significant predictors. Interpretation & conclusions: A significant increase in the number of dengue cases has occurred when the minimum temperature was 23.0-25.8°C and the cumulative rainfall 118.14-611.64 mm across the Indian districts. Further in-depth investigations incorporating more number of demographic, ecological and socio-economic factors would be needed for robust conclusions.

Spatial evaluation of prevalence, pattern and predictors of cervical cancer screening in India.

OBJECTIVE: To investigate the social determinants of cervical cancer screening and report the locations vulnerable to poor utilization of cervical cancer screening services. STUDY DESIGN: An ecological study with the data derived from fourth round of the National Family Health Survey conducted in India in the period 2015-2016. METHODS: The study focused on the percentage of women who have never undergone cervical cancer screening across 639 districts in India. Moran's I statistic was used to investigate the overall clustering of location. The Getis-Ord Gi* statistic was used for the detection of significant local clusters. Spatial error, spatial lag, spatial Durbin and spatial Durbin error models were compared, and the model with best fit was reported. ArcGIS, GeoDa and R software were used for the analysis. RESULTS: The existence of spatial autocorrelation (Moran's I = 0.61) necessitates the consideration of spatial component while studying the screening data. A significant clustering of districts with poor screening has been observed in the North-Central and North-Eastern regions of India. The geographic arrangement of the percentage of women who have undergone cervical cancer screening was associated with the percentage of women with poor wealth index (P < 0.001), not using a modern method of contraception (P < 0.001), residing in rural areas (P = 0.033) and never heard of sexually transmitted infection (P = 0.014). The range of percentage of women getting cervix screened for cancer was 0.5-68.4%, presenting the heterogeneity among the population elements. CONCLUSION: A higher risk of poor cervical cancer screening is observed in the districts where most of the women have poor wealth index, reside in urban area, have never heard of sexually transmitted infection and do not use a modern method of contraception.